import matplotlib.pyplot as plt
import math
from IPython.display import IFrame
import ipyplot
final_string = ""
for img in input("paste files ").split(" "):
# if "pdf" in img:
# final_string += f'IFrame("{img}", width = "1152px", height = "580px")\n\n'
# else:
final_string += f'<img src="./images/{img}" alt= “” width="1000px">\n\n'
print(final_string)
paste files <img src="./images/" alt= “” width="1000px">
def grid(ims, lbls = None, img_width=500, show_url = False):
if lbls is not None:
ipyplot.plot_images(ims, labels = lbls,img_width=img_width,show_url=show_url)
else:
ipyplot.plot_images(ims,img_width=img_width,show_url=show_url)
Proxy Attention : Approximating Attention in CNNs using Gradient Based Techniques
Subhaditya Mukherjee
Supervisors: S.H. Mohades Kasaei and Matias Valdenegro
ims = ["./images/cmuff.jpg","./images/class2.png","./images/class3.jpg","./images/class4.png"]
ims = ["./images/cifar100.pdf.png", "./images/caltech101.pdf.png", "./images/places256.pdf.png",
"./images/dogs.pdf.png", "./images/tsing.png"]
lbls = ["CIFAR100", "Caltech101", "Places 256", "Stanford Dogs","Tsinghua Dogs"]
ims = ["./images/vggarch.png", "./images/resnetarch.png", "./images/effnetarch.png","./images/vitarch.png"]
lbls = ["VGG16", "Resnet18", "EfficientNet B0", "ViT Base Patch 16x224"]
ims = ["./images/res1.png", "./images/res2.png", "./images/res3.png", "./images/res4.png"]
ims = ["./images/res5.png", "./images/res6.png", "./images/res7.png", "./images/res8.png"]
lbls = ["By Schedule", "By Proxy Threshold", "By Proxy Image Weight", "By Proxy Image Subset"]
ims = ["./images/expl2.png", "./images/expl3.png"]